{"id":"W1983015616","doi":"10.1021/ie8011566","title":"MPC Constraint Analysis—Bayesian Approach via a Continuous-Valued Profit Function","year":2009,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Computer science; Model predictive control; Constraint (computer-aided design); Probabilistic logic; Inference; Profit (economics); Variable elimination; Bayesian probability; Mathematics; Control (management); Artificial intelligence; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009274548,0.0003371449,0.0005370392,0.0003369681,0.0001086219,0.0001434676,0.0003370825,0.000517314,0.00007840287],"category_scores_gemma":[0.0002922626,0.0003773883,0.0001733256,0.002017891,0.00005837563,0.0001974461,0.00003280208,0.001302237,0.00001359152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004325414,"about_ca_system_score_gemma":0.00006964504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001408646,"about_ca_topic_score_gemma":4.053559e-7,"domain_scores_codex":[0.9973511,0.00005840078,0.0005482525,0.0004808657,0.0007157766,0.0008456206],"domain_scores_gemma":[0.9987861,0.00009813888,0.00005748514,0.000546294,0.0002299004,0.0002820835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004118731,0.00002290885,0.00009707202,0.00004945537,0.0003277764,0.000009068343,0.0000351134,0.6718115,0.324176,0.00009426499,0.000222194,0.003113477],"study_design_scores_gemma":[0.001390822,0.00006235774,0.0001289036,0.00005302816,0.0001423011,0.00001795485,0.00008064773,0.9482048,0.04859911,0.00004537803,0.0008344174,0.0004402832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07892231,0.0005621769,0.8965698,0.0001543848,0.0005263374,0.001937278,0.00004409114,0.002417106,0.01886644],"genre_scores_gemma":[0.99762,0.000005477346,0.0007022027,0.00000428173,0.000944858,0.0001200982,0.0001262173,0.00005882523,0.0004180784],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9186977,"threshold_uncertainty_score":0.9998678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03360004275262677,"score_gpt":0.2771454501310522,"score_spread":0.2435454073784254,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}